Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australias WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australias Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this bands value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scenes resolution. Currently WASARD supports water detection on scenes from ESAs Sentinel-1 and JAXAs ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics: Precision: a measure of what percentage of the points WASARD labels as water are truly water Recall: a measure of what percentage of the total water cover WASARD was able to identify. F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithms classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are water or not water as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarizations lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled Water and Not Water is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learns LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifiers accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifiers coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. Whil

COMMODITY PRICE AND INFLATION DYNAMICS: EVIDENCE FROM BRIICS

In this study, we use a commodity augmented Phillips curve to investigate the impact of global commodity prices on domestic inflation in Brazil, Russia, India, Indonesia, China, and South Africa. Oil and energy prices cause inflationary pressures in all countries, except Russia, where they cause deflationary pressures. In Indiaand Indonesia, global food prices are highly significant and positively related to inflation, while in South Africa precious metal prices impact inflation negatively. For policymakers, this study provides insights on the domestic adjustments required for inflation targeting in response to global commodity price volatility.In this study, we use a commodity augmented Phillips curve to investigate the impact of global commodity prices on domestic inflation in Brazil, Russia, India, Indonesia, China, and South Africa. Oil and energy prices cause inflationary pressures in all countries, except Russia, where they cause deflationary pressures. In Indiaand Indonesia, global food prices are highly significant and positively related to inflation, while in South Africa precious metal prices impact inflation negatively. For policymakers, this study provides insights on the domestic adjustments required for inflation targeting in response to global commodity price volatility

Tripartite analysis across business cycles in Turkey: A multi-timescale inquiry of efficiency, volatility and integration

AbstractIn the current era of globalization, deregulation and liberalization of markets have led to financial integration amongst developing and developed countries. The sudden massive inflow of capital into developing country's stock markets begs the question of whether or not the markets are sufficiently efficient to handle the increasing integration of markets. Furthermore, the relationship between the integration and efficiency of stock markets tends to be of greater importance during economic downturns. Taking Turkey as a case study owing to its economic growth and importance in two successful blocs, i.e. the EU and the OIC, we attempt to analyse the linkages between stock market efficiency and integration during the different phases of the economy. The findings of our study provide an interesting insight into the relative improvement in volatility, efficiency and integration across business cycles, in a multi time scale analysis

Lessons Learned and Cost Analysis of Hosting a Full Stack Open Data Cube (ODC) Application on the Amazon Web Services (AWS)

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary and cost analysis of our experience using Amazon Web Services (AWS) to host one such software product, the CEOS Data Cube (CDC) web-based User Interface (UI). In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The UI has empowered users by providing features that assist with streamlining data preparation, data processing, data visualization, and the sub-setting of Analysis Ready Data (ARD) products in order to achieve a wide variety of Earth imaging objectives

UNDERSTANDING MARKET REACTION TO COVID-19 MONETARY AND FISCAL STIMULUS IN MAJOR ASEAN COUNTRIES

In this paper, we examine the effect of fiscal and monetary policy stimulus actions during the COVID-19 pandemic on the stock markets of four ASEAN countries, namely, Indonesia, Singapore, Malaysia, and Thailand. Using time-series regression models, we show the relative importance of monetary and fiscal policies. Our findings suggest that 7-days after the policy announcement, fiscal policies helped cushion financial market losses in Indonesia, Singapore and Thailand. We do not find any robust evidence of policy effectiveness for Malaysia. While our investigation is preliminary it opens an additional avenue for understanding the effectiveness of policy stimulus.In this paper, we examine the effect of fiscal and monetary policy stimulus actions during the COVID-19 pandemic on the stock markets of four ASEAN countries, namely, Indonesia, Singapore, Malaysia, and Thailand. Using time-series regression models, we show the relative importance of monetary and fiscal policies. Our findings suggest that 7-days after the policy announcement, fiscal policies helped cushion financial market losses in Indonesia, Singapore and Thailand. We do not find any robust evidence of policy effectiveness for Malaysia. While our investigation is preliminary it opens an additional avenue for understanding the effectiveness of policy stimulus

Vehicular Ad Hoc Networks

(First paragraph) Vehicular ad hoc networks (VANETs) have recently been proposed as one of the promising ad hoc networking techniques that can provide both drivers and passengers with a safe and enjoyable driving experience. VANETs can be used for many applications with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In the United States, motor vehicle traffic crashes are the leading cause of death for all motorists between two and thirty-four years of age. In 2009, the National Highway Traffic Safety Administration (NHTSA) reported that 33,808 people were killed in motor vehicle traffic crashes. The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. The US-DOT also notes that in a single year, congested highways due to traffic incidents cost over $75 billion in lost worker productivity and over 8.4 billion gallons of fuel. Some of the significant applications of VANETs are road safety applications including collision and other safety warning systems, driver convenience and information systems, and, in the future, intelligent traffic management systems

Snow Observations from Space: an Approach to Map Snow Cover from Three Decades of Landsat Imagery Across Switzerland

Snow accumulation is one of the most important forms of water storage. The natural cycle of water is being increasingly influenced by climate change and will continue to change in the future. To understand the evolution of snow cover and to perfect its accurate detection UN Environment/GRID-Geneva and the University of Geneva have developed a Snow Detection tool called Snow Observations from Space for the Swiss Data Cube. The Snow Detection tool uses the C Function of Mask to identify snow pixels and then subsequently produces a normalized detection raster. Through further development, this tool will reach its full potential as an accurate method of detecting snow cover change for Switzerland

Bilateral retractile testis: A possible risk factor for male infertility

Healthy youth is an asset and strength for economic and social growth of any society. Developmental changes during puberty of youngsters need proper guidance to avoid future physical, physiological and psychological ailments. Bilateral Retractile Testis (BRT) is a health problem if remains unnoticed till adolescence can lead to tribulations. BRT may affect testicular parenchyma and spermatogenesis. Cremasteric muscles (muscles attached with testes) pull the testes up into groin so it randomly moves up and down and do not reside in its natural pouch always. It has been a controversial issue in male infertility as retraction may affect infertility. Objective: To estimate the prevalence of male infertility among patients with Bilateral Retractile Testis. Methodology: A total of 200 male patients were enrolled after the power calculation, with different reproductive health problems between the age of 18 - 40 years during the year 2016 at Reproductive health clinic Jinnah Post Graduate Medical Center (JPMC), Karachi. 25 male patients were reported with infertility and BRT. The data was collected using self-developed questionnaire including outdoor patients’ physical check-up, manual testicular examination as size and shape were done. Patient height, weight, blood pressure, their lifestyle, and dressing regarding wearing tight jeans habits were asked. Semen analysis was carried out showing abnormality in sperm count, motility or morphology and entered and analysed in the SPSS version 10. Means and percentages were calculated. Result: Out of 200 male infertile patients, a total of 25 (12.5%) patients with a diagnosis of BRT were found in the sample with the mean age of 29 years. Moreover, it was identified that those having BRT also have low motility of the sperms. Based on the results it was concluded that BRT is a possible cause of male infertility in Pakistani population. Conclusion: BRT is a possible risk factor to affect sperm motility and low sperm motility can lead to male infertility. The study estimates the prevalence of 12.5% among our sampled men

Serum levels of pancreatic stone protein (PSP)/reg1A as an indicator of beta-cell apoptosis suggest an increased apoptosis rate in hepatocyte nuclear factor 1 alpha (HNF1A-MODY) carriers from the third decade of life onward

Background Mutations in the transcription factor hepatocyte nuclear factor-1-alpha (HNF1A) result in the commonest type of maturity onset diabetes of the young (MODY). HNF1A-MODY carriers have reduced pancreatic beta cell mass, partially due to an increased rate of apoptosis. To date, it has not been possible to determine when apoptosis is occurring in HNF1A-MODY.We have recently demonstrated that beta cell apoptosis stimulates the expression of the pancreatic stone protein/regenerating (PSP/reg) gene in surviving neighbour cells, and that PSP/reg1A protein is subsequently secreted from these cells. The objective of this study was to determine whether serum levels of PSP/reg1A are elevated during disease progression in HNF1A-MODY carriers, and whether it may provide information regarding the onset of beta-cell apoptosis. Methods We analysed serum PSP/reg1A levels and correlated with clinical and biochemical parameters in subjects with HNF1A-MODY, glucokinase (GCK-MODY), and type 1 diabetes mellitus. A control group of normoglycaemic subjects was also analysed. Results PSP/reg1A serum levels were significantly elevated in HNF1A-MODY (n = 37) subjects compared to controls (n = 60) (median = 12.50 ng/ml, IQR = 10.61-17.87 ng/ml versus median = 10.72 ng/ml, IQR = 8.94-12.54 ng/ml, p = 0.0008). PSP/reg1A correlated negatively with insulin levels during OGTT, (rho = −0.40, p = 0.02). Interestingly we noted a significant positive correlation of PSP/reg1A with age of the HNF1A-MODY carriers (rho = 0.40 p = 0.02) with an age of 25 years separating carriers with low and high PSP/reg1A levels. Patients with type 1 diabetes mellitus also had elevated serum levels of PSP/reg1A compared to controls, however this was independent of the duration of diabetes. Conclusion Our data suggest that beta cell apoptosis contributes increasingly to the pathophysiology of HNF1A-MODY in patients 25 years and over. PSP/reg1A may be developed as a serum marker to detect increased beta-cell apoptosis, or its therapeutic response